Topics include:

• Core syntax, functions, type hints & virtual environments

• Data wrangling with Pandas and interactive dashboards via Plotly

• Automation from the CLI and scheduled scripts

• High-performance, async APIs with FastAPI and modern concurrency patterns

• Packaging, testing, and shipping production-ready code

Ideal for: developers, data analysts, and engineers with basic programming experience who need Python skills for production-grade data and software projects.

Topics include:

• Interacting with data using Pandas

• Visual storytelling in Seaborn and Plotly

• Building classical models in scikit-learn: regression, classification, clustering

• Tracking training experiments via MLflow

• Evaluating models

• Packaging models and exposing prediction APIs using FastAPI

• Deploying to cloud and monitoring performance

Ideal for: analysts, devs, and researchers comfortable with Python who want to learn how to take ML models from notebook to production

Topics include:

• Neural-net foundations

• Vision & sequence models: CNNs, RNNs, Transformers

• Modern generative tech: diffusion pipelines (e.g., Stable Diffusion) & LLMs

• Retrieval-Augmented Generation (RAG) and agent orchestration with LangChain

Ideal for: engineers & data scientists already fluent in Python & scikit-learn who need to build and deploy Gen-AI solutions.

Topics include:

• Git-centric workflow & automated CI/CD (Gitlab, Jenkins, Github)

• Containerisation with Docker, orchestration on Kubernetes & Docker Compose

• Experiment tracking (MLflow), feature stores

• ETL & orchestration (Airflow)

• Infrastructure as Code (Terraform)

Ideal for: data scientists and ML engineers looking to productionise models at scale.s here